Nlvr2

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+### Added
+
+- Added support for NLVR2 visual entailment, including a data loader, two models, and training configs.
+
 
 ## [v2.5.0](https://github.com/allenai/allennlp-models/releases/tag/v2.5.0) - 2021-06-03
 

README.md

@@ -153,6 +153,8 @@ Here is a list of pre-trained models currently available.
 - [`mc-roberta-commonsenseqa`](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/modelcards/mc-roberta-commonsenseqa.json) - RoBERTa-based multiple choice model for CommonSenseQA.
 - [`mc-roberta-piqa`](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/modelcards/mc-roberta-piqa.json) - RoBERTa-based multiple choice model for PIQA.
 - [`mc-roberta-swag`](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/modelcards/mc-roberta-swag.json) - RoBERTa-based multiple choice model for SWAG.
+- [`nlvr2-vilbert`](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/modelcards/nlvr2-vilbert-head.json) - ViLBERT-based model for Visual Entailment.
+- [`nlvr2-vilbert`](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/modelcards/nlvr2-vilbert.json) - ViLBERT-based model for Visual Entailment.
 - [`pair-classification-binary-gender-bias-mitigated-roberta-snli`](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/modelcards/pair-classification-binary-gender-bias-mitigated-roberta-snli.json) - RoBERTa finetuned on SNLI with binary gender bias mitigation.
 - [`pair-classification-decomposable-attention-elmo`](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/modelcards/pair-classification-decomposable-attention-elmo.json) - The decomposable attention model (Parikh et al, 2017) combined with ELMo embeddings trained on SNLI.
 - [`pair-classification-esim`](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/modelcards/pair-classification-esim.json) - Enhanced LSTM trained on SNLI.

allennlp_models/modelcards/nlvr2-vilbert-head.json

@@ -0,0 +1,66 @@
+{
+    "id": "nlvr2-vilbert",
+    "registered_model_name": "nlvr2",
+    "registered_predictor_name": null,
+    "display_name": "Visual Entailment - NLVR2",
+    "task_id": "nlvr2",
+    "model_details": {
+        "description": "This model is based on the ViLBERT architecture. The image features are obtained using the ResNet backbone and Faster RCNN (region detection).",
+        "short_description": "ViLBERT-based model for Visual Entailment.",
+        "developed_by": "Lu et al",
+        "contributed_by": "Jacob Morrison",
+        "date": "2021-05-27",
+        "version": "2",
+        "model_type": "ViLBERT based on BERT large",
+        "paper": {
+            "citation": "\n@inproceedings{Lu2019ViLBERTPT,\ntitle={ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks},\nauthor={Jiasen Lu and Dhruv Batra and D. Parikh and Stefan Lee},\nbooktitle={NeurIPS},\nyear={2019}",
+            "title": "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks",
+            "url": "https://api.semanticscholar.org/CorpusID:199453025"
+        },
+        "license": null,
+        "contact": "[email protected]"
+    },
+    "intended_use": {
+        "primary_uses": "This model is developed for the AllenNLP demo.",
+        "primary_users": null,
+        "out_of_scope_use_cases": null
+    },
+    "factors": {
+        "relevant_factors": null,
+        "evaluation_factors": null
+    },
+    "metrics": {
+        "model_performance_measures": "Accuracy and F1-score",
+        "decision_thresholds": null,
+        "variation_approaches": null
+    },
+    "evaluation_data": {
+        "dataset": {
+            "name": "Natural Language for Visual Reasoning For Real dev set",
+            "url": "https://github.com/lil-lab/nlvr/tree/master/nlvr2",
+            "notes": "Evaluation requires a large amount of images to be accessible locally, so we cannot provide a command you can easily copy and paste."
+        },
+        "motivation": null,
+        "preprocessing": null
+    },
+    "training_data": {
+        "dataset": {
+            "name": "Natural Language for Visual Reasoning For Real train set",
+            "url": "https://github.com/lil-lab/nlvr/tree/master/nlvr2"
+        },
+        "motivation": null,
+        "preprocessing": null
+    },
+    "quantitative_analyses": {
+        "unitary_results": "On the validation set:\nF1: 33.7%\nAccuracy: 50.8%.\nThese scores do not match the performance in the 12-in-1 paper because this was trained as a standalone task, not as part of a multitask setup. Please contact us if you want to match those scores!",
+        "intersectional_results": null
+    },
+    "model_ethical_considerations": {
+        "ethical_considerations": null
+    },
+    "model_usage": {
+        "archive_file": "vilbert-nlvr2-head-2021.06.01.tar.gz",
+        "training_config": "vilbert_nlvr2_pretrained.jsonnet",
+        "install_instructions": "pip install allennlp>=2.5.1 allennlp-models>=2.5.1"
+    }
+}

allennlp_models/modelcards/nlvr2-vilbert.json

@@ -0,0 +1,66 @@
+{
+    "id": "nlvr2-vilbert",
+    "registered_model_name": "nlvr2",
+    "registered_predictor_name": null,
+    "display_name": "Visual Entailment - NLVR2",
+    "task_id": "nlvr2",
+    "model_details": {
+        "description": "This model is based on the ViLBERT multitask architecture. The image features are obtained using the ResNet backbone and Faster RCNN (region detection).",
+        "short_description": "ViLBERT-based model for Visual Entailment.",
+        "developed_by": "Lu et al",
+        "contributed_by": "Jacob Morrison",
+        "date": "2021-05-27",
+        "version": "2",
+        "model_type": "ViLBERT based on BERT large",
+        "paper": {
+            "citation": "\n@inproceedings{Lu2019ViLBERTPT,\ntitle={ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks},\nauthor={Jiasen Lu and Dhruv Batra and D. Parikh and Stefan Lee},\nbooktitle={NeurIPS},\nyear={2019}",
+            "title": "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks",
+            "url": "https://api.semanticscholar.org/CorpusID:199453025"
+        },
+        "license": null,
+        "contact": "[email protected]"
+    },
+    "intended_use": {
+        "primary_uses": "This model is developed for the AllenNLP demo.",
+        "primary_users": null,
+        "out_of_scope_use_cases": null
+    },
+    "factors": {
+        "relevant_factors": null,
+        "evaluation_factors": null
+    },
+    "metrics": {
+        "model_performance_measures": "Accuracy and F1-score",
+        "decision_thresholds": null,
+        "variation_approaches": null
+    },
+    "evaluation_data": {
+        "dataset": {
+            "name": "Natural Language for Visual Reasoning For Real dev set",
+            "url": "https://github.com/lil-lab/nlvr/tree/master/nlvr2",
+            "notes": "Evaluation requires a large amount of images to be accessible locally, so we cannot provide a command you can easily copy and paste."
+        },
+        "motivation": null,
+        "preprocessing": null
+    },
+    "training_data": {
+        "dataset": {
+            "name": "Natural Language for Visual Reasoning For Real train set",
+            "url": "https://github.com/lil-lab/nlvr/tree/master/nlvr2"
+        },
+        "motivation": null,
+        "preprocessing": null
+    },
+    "quantitative_analyses": {
+        "unitary_results": "On the validation set:\nF1: 33.7%\nAccuracy: 50.8%.\nThese scores do not match the performance in the 12-in-1 paper because this was trained as a standalone task, not as part of a multitask setup. Please contact us if you want to match those scores!",
+        "intersectional_results": null
+    },
+    "model_ethical_considerations": {
+        "ethical_considerations": null
+    },
+    "model_usage": {
+        "archive_file": "vilbert-nlvr2-2021.06.01.tar.gz",
+        "training_config": "vilbert_nlvr2_pretrained.jsonnet",
+        "install_instructions": "pip install allennlp>=2.5.1 allennlp-models>=2.5.1"
+    }
+}

allennlp_models/vision/dataset_readers/__init__.py

@@ -1,5 +1,6 @@
 from allennlp_models.vision.dataset_readers.vision_reader import VisionReader
 from allennlp_models.vision.dataset_readers.gqa import GQAReader
+from allennlp_models.vision.dataset_readers.nlvr2 import Nlvr2Reader
 from allennlp_models.vision.dataset_readers.vgqa import VGQAReader
 from allennlp_models.vision.dataset_readers.vqav2 import VQAv2Reader
 from allennlp_models.vision.dataset_readers.visual_entailment import VisualEntailmentReader

allennlp_models/vision/dataset_readers/nlvr2.py

@@ -0,0 +1,220 @@
+import logging
+from os import PathLike
+from typing import Any, Dict, Iterable, Tuple, Union, Optional
+
+from overrides import overrides
+import torch
+from torch import Tensor
+
+from allennlp.common.file_utils import cached_path, json_lines_from_file
+from allennlp.common.lazy import Lazy
+from allennlp.data.dataset_readers.dataset_reader import DatasetReader
+from allennlp.data.fields import ArrayField, LabelField, ListField, MetadataField, TextField
+from allennlp.data.image_loader import ImageLoader
+from allennlp.data.instance import Instance
+from allennlp.data.token_indexers import TokenIndexer
+from allennlp.data.tokenizers import Tokenizer
+from allennlp.modules.vision.grid_embedder import GridEmbedder
+from allennlp.modules.vision.region_detector import RegionDetector
+
+from allennlp_models.vision.dataset_readers.vision_reader import VisionReader
+
+logger = logging.getLogger(__name__)
+
+
+@DatasetReader.register("nlvr2")
+class Nlvr2Reader(VisionReader):
+    """
+    Reads the NLVR2 dataset from [http://lil.nlp.cornell.edu/nlvr/](http://lil.nlp.cornell.edu/nlvr/).
+    In this task, the model is presented with two images and a hypothesis referring to those images.
+    The task for the model is to identify whether the hypothesis is true or false.
+    Accordingly, the instances produced by this reader contain two images, featurized into the
+    fields "box_features" and "box_coordinates". In addition to that, it produces a `TextField`
+    called "hypothesis", and a `MetadataField` called "identifier". The latter contains the question
+    id from the question set.
+
+    Parameters
+    ----------
+    image_dir: `str`
+        Path to directory containing `png` image files.
+    image_loader: `ImageLoader`
+        An image loader to read the images with
+    image_featurizer: `GridEmbedder`
+        The backbone image processor (like a ResNet), whose output will be passed to the region
+        detector for finding object boxes in the image.
+    region_detector: `RegionDetector`
+        For pulling out regions of the image (both coordinates and features) that will be used by
+        downstream models.
+    feature_cache_dir: `str`, optional
+        If given, the reader will attempt to use the featurized image cache in this directory.
+        Caching the featurized images can result in big performance improvements, so it is
+        recommended to set this.
+    tokenizer: `Tokenizer`, optional, defaults to `PretrainedTransformerTokenizer("bert-base-uncased")`
+    token_indexers: `Dict[str, TokenIndexer]`, optional,
+        defaults to`{"tokens": PretrainedTransformerIndexer("bert-base-uncased")}`
+    cuda_device: `int`, optional
+        Set this to run image featurization on the given GPU. By default, image featurization runs on CPU.
+    max_instances: `int`, optional
+        If set, the reader only returns the first `max_instances` instances, and then stops.
+        This is useful for testing.
+    image_processing_batch_size: `int`
+        The number of images to process at one time while featurizing. Default is 8.
+    """
+
+    def __init__(
+        self,
+        image_dir: Optional[Union[str, PathLike]] = None,
+        *,
+        image_loader: Optional[ImageLoader] = None,
+        image_featurizer: Optional[Lazy[GridEmbedder]] = None,
+        region_detector: Optional[Lazy[RegionDetector]] = None,
+        feature_cache_dir: Optional[Union[str, PathLike]] = None,
+        tokenizer: Optional[Tokenizer] = None,
+        token_indexers: Optional[Dict[str, TokenIndexer]] = None,
+        cuda_device: Optional[Union[int, torch.device]] = None,
+        max_instances: Optional[int] = None,
+        image_processing_batch_size: int = 8,
+        write_to_cache: bool = True,
+    ) -> None:
+        run_featurization = image_loader and image_featurizer and region_detector
+        if image_dir is None and run_featurization:
+            raise ValueError(
+                "Because of the size of the image datasets, we don't download them automatically. "
+                "Please go to https://github.com/lil-lab/nlvr/tree/master/nlvr2, download the datasets you need, "
+                "and set the image_dir parameter to point to your download location. This dataset "
+                "reader does not care about the exact directory structure. It finds the images "
+                "wherever they are."
+            )
+
+        super().__init__(
+            image_dir,
+            image_loader=image_loader,
+            image_featurizer=image_featurizer,
+            region_detector=region_detector,
+            feature_cache_dir=feature_cache_dir,
+            tokenizer=tokenizer,
+            token_indexers=token_indexers,
+            cuda_device=cuda_device,
+            max_instances=max_instances,
+            image_processing_batch_size=image_processing_batch_size,
+            write_to_cache=write_to_cache,
+        )
+
+        github_url = "https://raw.githubusercontent.com/lil-lab/nlvr/"
+        nlvr_commit = "68a11a766624a5b665ec7594982b8ecbedc728c7"
+        data_dir = f"{github_url}{nlvr_commit}/nlvr2/data"
+        self.splits = {
+            "dev": f"{data_dir}/dev.json",
+            "test": f"{data_dir}/test1.json",
+            "train": f"{data_dir}/train.json",
+            "balanced_dev": f"{data_dir}/balanced/balanced_dev.json",
+            "balanced_test": f"{data_dir}/balanced/balanced_test1.json",
+            "unbalanced_dev": f"{data_dir}/balanced/unbalanced_dev.json",
+            "unbalanced_test": f"{data_dir}/balanced/unbalanced_test1.json",
+        }
+
+    @overrides
+    def _read(self, split_or_filename: str):
+        filename = self.splits.get(split_or_filename, split_or_filename)
+
+        json_file_path = cached_path(filename)
+
+        blobs = []
+        json_blob: Dict[str, Any]
+        for json_blob in json_lines_from_file(json_file_path):
+            blobs.append(json_blob)
+
+        blob_dicts = list(self.shard_iterable(blobs))
+        processed_images1: Iterable[Optional[Tuple[Tensor, Tensor]]]
+        processed_images2: Iterable[Optional[Tuple[Tensor, Tensor]]]
+        if self.produce_featurized_images:
+            # It would be much easier to just process one image at a time, but it's faster to process
+            # them in batches. So this code gathers up instances until it has enough to fill up a batch
+            # that needs processing, and then processes them all.
+
+            try:
+                image_paths1 = []
+                image_paths2 = []
+                for blob in blob_dicts:
+                    identifier = blob["identifier"]
+                    image_name_base = identifier[: identifier.rindex("-")]
+                    image_paths1.append(self.images[f"{image_name_base}-img0.png"])
+                    image_paths2.append(self.images[f"{image_name_base}-img1.png"])
+            except KeyError as e:
+                missing_id = e.args[0]
+                raise KeyError(
+                    missing_id,
+                    f"We could not find an image with the id {missing_id}. "
+                    "Because of the size of the image datasets, we don't download them automatically. "
+                    "Please go to https://github.com/lil-lab/nlvr/tree/master/nlvr2, download the "
+                    "datasets you need, and set the image_dir parameter to point to your download "
+                    "location. This dataset reader does not care about the exact directory "
+                    "structure. It finds the images wherever they are.",
+                )
+
+            processed_images1 = self._process_image_paths(image_paths1)
+            processed_images2 = self._process_image_paths(image_paths2)
+        else:
+            processed_images1 = [None for _ in range(len(blob_dicts))]
+            processed_images2 = [None for _ in range(len(blob_dicts))]
+
+        attempted_instances = 0
+        for json_blob, image1, image2 in zip(blob_dicts, processed_images1, processed_images2):
+            identifier = json_blob["identifier"]
+            hypothesis = json_blob["sentence"]
+            label = json_blob["label"] == "True"
+            instance = self.text_to_instance(identifier, hypothesis, image1, image2, label)
+            if instance is not None:
+                attempted_instances += 1
+                yield instance
+        logger.info(f"Successfully yielded {attempted_instances} instances")
+
+    def extract_image_features(self, image: Union[str, Tuple[Tensor, Tensor]], use_cache: bool):
+        if isinstance(image, str):
+            features, coords = next(self._process_image_paths([image], use_cache=use_cache))
+        else:
+            features, coords = image
+
+        return (
+            ArrayField(features),
+            ArrayField(coords),
+            ArrayField(
+                features.new_ones((features.shape[0],), dtype=torch.bool),
+                padding_value=False,
+                dtype=torch.bool,
+            ),
+        )
+
+    @overrides
+    def text_to_instance(
+        self,  # type: ignore
+        identifier: Optional[str],
+        hypothesis: str,
+        image1: Union[str, Tuple[Tensor, Tensor]],
+        image2: Union[str, Tuple[Tensor, Tensor]],
+        label: bool,
+        use_cache: bool = True,
+    ) -> Instance:
+        hypothesis_field = TextField(self._tokenizer.tokenize(hypothesis), None)
+        box_features1, box_coordinates1, box_mask1 = self.extract_image_features(image1, use_cache)
+        box_features2, box_coordinates2, box_mask2 = self.extract_image_features(image2, use_cache)
+
+        fields = {
+            "hypothesis": ListField([hypothesis_field, hypothesis_field]),
+            "box_features": ListField([box_features1, box_features2]),
+            "box_coordinates": ListField([box_coordinates1, box_coordinates2]),
+            "box_mask": ListField([box_mask1, box_mask2]),
+        }
+
+        if identifier is not None:
+            fields["identifier"] = MetadataField(identifier)
+
+        if label is not None:
+            fields["label"] = LabelField(int(label), skip_indexing=True)
+
+        return Instance(fields)
+
+    @overrides
+    def apply_token_indexers(self, instance: Instance) -> None:
+        instance["hypothesis"][0].token_indexers = self._token_indexers  # type: ignore
+        instance["hypothesis"][1].token_indexers = self._token_indexers  # type: ignore

allennlp_models/vision/models/__init__.py

@@ -1,3 +1,4 @@
+from allennlp_models.vision.models.nlvr2 import Nlvr2Model
 from allennlp_models.vision.models.vision_text_model import VisionTextModel
 from allennlp_models.vision.models.visual_entailment import VisualEntailmentModel
 from allennlp_models.vision.models.vilbert_vqa import VqaVilbert

allennlp_models/vision/models/heads/__init__.py

@@ -1,2 +1,3 @@
+from allennlp_models.vision.models.heads.nlvr2_head import Nlvr2Head
 from allennlp_models.vision.models.heads.vqa_head import VqaHead
 from allennlp_models.vision.models.heads.visual_entailment_head import VisualEntailmentHead